Access the full text.
Sign up today, get DeepDyve free for 14 days.
Chet Haase, G. Meyer (1992)
Modeling pigmented materials for realistic image synthesisACM Trans. Graph., 11
J. Saunderson (1942)
Calculation of the Color of Pigmented PlasticsJournal of the Optical Society of America, 32
F. Bondioli, T. Manfredini, M. Romagnoli (2006)
Color matching algorithms in ceramic tile productionJournal of The European Ceramic Society, 26
William Vargas, G. Niklasson (1997)
Applicability conditions of the Kubelka-Munk theory.Applied optics, 36 22
E. Allen (1966)
Basic Equations Used in Computer Color MatchingJournal of the Optical Society of America, 56
R. Marcus, P. Pierce (1994)
An analysis of the first surface correction for the color matching of organic coatings from the viewpoint of radiative transfer theoryProgress in Organic Coatings, 23
Paul Kubelka (1948)
New Contributions to the Optics of Intensely Light-Scattering Materials. Part IJournal of the Optical Society of America, 38
Li Yang, B. Kruse (2004)
Revised Kubelka-Munk theory. I. Theory and application.Journal of the Optical Society of America. A, Optics, image science, and vision, 21 10
Nobbs Nobbs (1997)
Colour‐match prediction for pigmented materialsJ Soc Dyers Colourists, 34
Park Park, Stearns Stearns (1944)
Spectrophotometric formulationJ Opt Soc Am, 34
H. Davidson, H. Hemmendinger (1966)
Color Prediction Using the Two-Constant Turbid-Media Theory*Journal of the Optical Society of America, 56
W. Vargas (2002)
Inversion methods from Kubelka–Munk analysisJournal of Optics A: Pure and Applied Optics, 4
E. Allen (1974)
Basic equations used in computer color matching, II. Tristimulus match, two-constant theoryJournal of the Optical Society of America, 64
In this study, we propose a color mixing and color separation method for opaque surface made of the pigments dispersed in filling materials. The method is based on Kubelka–Munk model. Eleven different pigments with seven different concentrations have been used as training sets. The amount of concentration of each pigment in the mixture is estimated from the training sets by using the least‐square pseudo‐inverse calculation. The result depends on the number and type of pigments selected for calculation. At most we can select all pigments. The combinations resulted with negative concentrations or unusual high concentrations are discarded from the list of candidate combination. The optimal pigment's set and its concentrations are estimated by minimizing the reflectance difference of given reflectance and predicted reflectance. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 461–469, 2008
Color Research & Application – Wiley
Published: Dec 1, 2008
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.